In the continuing effort to make vehicles safer, more automakers are beginning to incorporate collision detection systems in their vehicles. Collision detection systems usually include an object tracking system that receives inputs from sensors mounted on a host vehicle. The sensors usually detect objects in the vicinity of the host vehicle. The object tracking system uses the information from the sensors to determine the track of the target object. The track of the target object generally includes information such as the position, velocity and acceleration of the target object relative to the host vehicle. While a collision detection system could use only a single sensor, systems generally include a plurality of sensors acting in concert to more accurately locate and track a target object. The object tracking system performs calculations using the information provided by the sensors to determine the information regarding the relative motion of the target object
The ranging sensors used by the collision detection system usually include a combination of short-range, mid-range and long-range sensors. Long-range and mid-range sensors often include radar systems and visioning systems, which are able to discern information regarding the distance to an object, as well as the size and shape of an object, while it is still a relatively long distance to the host vehicle. Long range sensors can also be LIDAR (Light Intensified Detection And Ranging) or LADAR (Light Amplified Detection And Ranging) sensors. These sensors provide advanced warning of target objects, allowing the objects to be tracked as the host vehicle approaches. Short range sensors are usually used to provide more accurate data regarding objects within the immediate vicinity of the host vehicle, to allow the most accurate possible readings prior to a collision. Short range sensors usually operate at a higher frequency than long range sensors, allowing the information provided therefrom to be updated more frequently. One common short range sensor is a LIDAR (Light Detection and Ranging) sensor. A LIDAR system uses lasers to provide an accurate, high-frequency signal identifying a close-range object.
To provide the best view, and consequently to obtain the best results, from the short range sensor, it is desirable to mount the sensors at the highest practical point on the vehicle. In practice, this usually leads to the short range sensor being mounted behind the windshield near the roof line, positioned on a slight downward angle. Mounting the sensor behind the windshield not only protects the sensor, but allows the sensor's view to be kept clean through the use of windshield wipers. The downward angle of the beam path is useful as it covers a broad vertical spectrum in front of the host vehicle. That is, the beam is capable of detecting objects even when they are very close to the ground. Further, beams from the short range sensors are likely to contact a target at the point closest to the host vehicle, providing accurate distancing information to the sensors. Long range sensors, especially LIDAR or vision sensors, are also sometimes mounted behind the windshield to protect the sensor and keep the view clean through the use of windshield wipers.
The object tracker system uses the information provided by the sensors to develop an accurate picture of the relative motion of the target object. Object tracker systems generally use a recursive filter to develop and refine estimations of the motion of target objects. One recursive filter which is often used in automotive applications is the Kalman filter. An advantage to using a recursive filter, such as the Kalman filter, is the ability of the filter to provide an accurate estimate of a property based on noisy input data, such as that provided by the sensors. The filter develops an initial estimate of the desired property or properties, such as position, velocity, acceleration, etc, and then compares the estimate with a subsequent sensor reading. These properties are called a state. The estimate is then refined based on mathematical estimates augmented by subsequent sensor readings to produce an even more accurate estimate. This cycle continues with each subsequent sensor reading to continually update the state estimation. Thus, as long as the sensor is providing accurate information to the filter, the estimate will continue to improve. When multiple sensors are available, multiple sensor readings can be used to improve estimates. However, when a near field target is detected by both a short range sensor and a long range sensor, the readings from the short range sensor will dominate the calculations performed by the recursive filter, as they are likely to be more accurate, and are updated more frequently.
Based on the estimated relative motion of an object, the collision detection system is able to estimate whether there is likely to be a collision between a host vehicle and the object. If such a collision is likely, the system is able to estimate when such a collision will occur (the “impact time”), and the relative velocity of the vehicles at that time (the “impact velocity”). This information can be used by a collision detection system to prepare or activate safety features in a vehicle, such as seat belt pretensioners and airbags, to help ensure the safety of the vehicle's occupants.
While the above methods have resulted in continually improved performance in collision avoidance systems, challenges remain and additional improvements are possible. Recursive filters in known systems continually update estimates of motion properties using readings from short range sensors until a collision actually occurs. While this may seem like an optimal situation, allowing the most accurate tracking of an object, this is not always the case. Subsequent estimates from the recursive filter are limited in their accuracy by the quality of the information received from the sensors. While sensors can provide accurate information in the area closely proximate the host vehicle, the geometries of the target objects can actually cause errant readings (i.e., with non-Gaussian errors) in the moments immediately preceding a collision.
As stated previously, short range sensors and some long range sensors are typically mounted high behind a windshield, pointed at a downward angle. Assuming the sensor is a short range LIDAR sensor, the sensor emits a series of beams. The beams travel downwardly until they impinge on a target, at which point they reflect back to the sensor. By analyzing the reflected signals, the distance to the target object can be determined, and refined, as explained above. Due in part to the downward pointed sensor and the plurality of beams, the sensor detects the point of the object which is closest to host vehicle. This allows the most accurate prediction of when the host vehicle will collide with the object (as the host vehicle will impact the object at the closest point). This works well as long as the sensor is able to “see” the closest point of the target object. However, when the host vehicle and the target object are within a certain distance of each other, the geometry of the beam from the host vehicle may be such that the beam from the sensor may not be able to impinge on the nearest point of the target. For instance, if the host vehicle is approaching a target vehicle from behind, the beam may initially impinge on the rear face of the vehicle. As the host vehicle gets closer, however, the beam may impinge on a rear contour of the vehicle, such as the upper, inwardly sloping surface of a trunk. As the vehicles become even closer, the beam may even impinge on the rear window. When the beam impinges on a spot other than the point nearest the host vehicle, the sensor readings will be incorrect. In other words, the sensor measurement errors are biased and not satisfying the Gaussian distribution assumed by the recursive filter. Consequently, estimates of the relative motion and position of the target object will be incorrect, as will estimates of impact time and impact velocity. This effect exists also for long range sensors mounted in the windshield. What is needed therefore is a collision detection system having improved short range performance to provide more accurate prediction of impact time and velocity.